This study is a Replication of research done by Chakraborty (2021). Chakraborty analyses county level data to find trends between disability rates and COVID-19 infection rates. The study uses Bivariate Pearson product moment correlation and Generalized Estimating functions to test for the aforementioned correlations in 18 different socio-demographic categories. The purpose of the study is to analyze whether people with disabilities face disproportionate outcomes from COVID-19. T
This study is a replication of:
Chakraborty, J. 2021. Social inequities in the distribution of COVID-19: An intra-categorical analysis of people with disabilities in the U.S. Disability and Health Journal 14:1-5. https://doi.org/10.1016/j.dhjo.2020.101007
Key words: COVID-19, Disability, Intersectionality,
Race/ethnicity, Poverty, ReproducibilitySubject: select from the BePress
TaxonomyDate created: date when project was startedDate modified: date of most recent revisionSpatial Coverage: Specify the geographic extent of your
study. This may be a place name and link to a feature in a gazetteer
like GeoNames or OpenStreetMap, or a well known text (WKT)
representation of a bounding box.Spatial Resolution: Specify the spatial resolution as a
scale factor, description of the level of detail of each unit of
observation (including administrative level of administrative areas),
and/or or distance of a raster GRID sizeSpatial Reference System: Specify the geographic or
projected coordinate system for the study, e.g. EPSG:4326Temporal Coverage: Specify the temporal extent of your
study—i.e. the range of time represented by the data observations.Temporal Resolution: Specify the temporal resolution of
your study—i.e. the duration of time for which each observation
represents or the revisit period for repeated observationsFunding Name: name of funding for the projectFunding Title: title of project grantAward info URI: web address for award informationAward number: award numberSpatial Coverage: extent of original studySpatial Resolution: resolution of original studySpatial Reference System: spatial reference system of
original studyTemporal Coverage: temporal extent of original
studyTemporal Resolution: temporal resolution of original
studyThe aim of this reproduction study is to implement the original study as closely as possible to reproduce the map of county level distribution of COVID-19 incidence rate, the summary statistics and bivariate correlation for disability characteristics and COVID-19 incidence.
Title: American Community Survey (ACS) five-year
estimate (2014-2018)Abstract: Sociodemographic breakdown of disabled
populationSpatial Coverage: United StatesSpatial Resolution: CountySpatial Representation Type: Vector MULTIPOLYGONSpatial Reference System: EPSG 4269Temporal Coverage: 2014-2018Temporal Resolution: five-year estimateLineage: Pulled documented variables from S1810 and
C18130 tables using tidyCensusDistribution: Publicly AvailableConstraints: Public DataThe American Community Survey (ACS) five-year estimate (2014-2018) variables used in the study are outlined in the table below. Details on ACS data collection can be found at https://www.census.gov/topics/health/disability/guidance/data-collection-acs.html and details on sampling methods and accuracy can be found at https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html.
| Variable Name in Study | ACS Variable name |
|---|---|
| percent of total civilian non-institutionalized population with a disability | S1810_C03_001E |
| Race | |
| percent w disability: White alone | S1810_C03_004E |
| percent w disability: Black alone | S1810_C03_005E |
| percent w disability: Native American | S1810_C03_006E |
| percent w disability: Asian alone | S1810_C03_007E |
| percent w disability: Other race | S1810_C03_009E |
| Ethnicity | |
| percent w disability: Non-Hispanic White | S1810_C03_0011E |
| percent w disability: Hispanic | S1810_C03_012E |
| percent w disability: Non-Hispanic non-White | (S1810_C02_001E - S1810_C02_011E - S1810_C02_012E) / (S1810_C01_001E - S1810_C01_011E - S1810_C01_012E) * 100 |
| percent w disability: Other race | S1810_C03_009E |
| Poverty | |
| percent w disability: Below poverty level | (C18130_004E + C18130_011E + C18130_018E) / C18130_001E * 100 |
| percent w disability: Above poverty level | (C18130_005E + C18130_012E + C18130_019E) / C18130_001E * 100 |
| Age | |
| percent w disability: 5-17 | S1810_C03_014E |
| percent w disability: 18-34 | S1810_C03_015E |
| percent w disability: 35-64 | S1810_C03_016E |
| percent w disability: 65-74 | S1810_C03_017E |
| percent w disability: 75+ | S1810_C03_018E |
| Biological sex | |
| percent w disability: male | S1810_C03_001E |
| percent w disability: female | S1810_C03_003E |
American Community Survey (ACS) data for sociodemographic
subcategories of people with disabilities can be accessed by using the
tidycensus package to query the Census API. This requires
an API key which can be acquired at api.census.gov/data/key_signup.html.
Title: County Level COVID-19 Incidence RateAbstract: Socioodemographic breakdown of disabled
populationSpatial Coverage: United StatesSpatial Resolution: CountySpatial Representation Type: Vector MULTIPOLYGONSpatial Reference System: EPSG 4269Temporal Coverage: 2020-01-22 — 2020-08-01Temporal Resolution: 8 monthsLineage: Center for System Science in Engineering at
Johns Hopkins University August, 01, 2020Distribution: see belowConstraints: Public DataData on COVID-19 cases from the Johns Hopkins University dashboard
have been provided directly with the research compendium because the
data is no longer available online in the state in which it was
downloaded on August 1, 2020. The dashboard and cumulative counts of
COVID-19 cases and deaths were continually updated, so an exact
reproduction required communication with the original author, Jayajit
Chakraborty, for assistance with provision of data from August 1, 2020.
The data includes an estimate of the total population
(POP_ESTIMA) and confirmed COVID-19 cases
(Confirmed). The COVID-19 case data expresses cumulative
count of reported COVID-19 from 1/22/2020 to 8/1/2020. Although metadata
for this particular resource is no longer available from the original
source, one can reasonably assume that the total population estimate was
based on the 2014-2018 5-year ACS estimate, as the 2019 estimates data
had not been released yet.
Versions of the data can be found at the John Hopkins CCSE COVID-19 Data Repository (https://github.com/CSSEGISandData/COVID-19). However, archived data only provides summaries at the national scale. We received the COVID-19 case data through 8/1/2020 at the county level from the author, as there is no readily apparent way to access archived data from the Johns Hopkins University Center for Systems Science Engineering database.
The original study extent is the lower 48 states and Washington D.C. Therefore, Alaska, Hawai’i and Puerto Rico are removed from the data (workflow step 1). Data on people with disabilities in poverty is derived from a different census table (C18130) than data on people with disabilities and age, race, ethnicity, age, and biological sex (S1810). Therefore, join the poverty data to the other data using the GEOID (workflow step 3). Also transform the ACS geographic data into Contiguous USA Albers Equal Area projection and fix geometry errors.
Optionally, save the raw ACS data to
data/raw/public/acs.gpkg for use in GIS software.
Calculate independent socio-demographic variables of people with disabilities as percentages for each sub-category of disability (race, ethnicity, poverty, age, and biological sex) and remove raw census data from the data frame (workflow step 4). Reproject the data into an Albers equal area conic projection.
Calculate the COVID incidence rate as the cases per 100,000 people (workflow step 2). Convert the COVID data to a non-geographic data frame.
Join dependent COVID data to independent ACS demographic data.
Unplanned deviation for reproduction: There is one county with missing disability and poverty data. This was not mentioned in the original study or in our pre-analyis plan. However, we replace the missing data with zeros, producing results identical to Chakraborty’s.
| fips | statefp | county | county_st | covid_rate | dis_pct | white_pct | black_pct | native_pct | asian_pct | other_pct | non_hisp_white_pct | hisp_pct | non_hisp_non_white_pct | bpov_pct | apov_pct | pct_5_17 | pct_18_34 | pct_35_64 | pct_65_74 | pct_75 | male_pct | female_pct | pop | cases | x | y |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35039 | 35 | Rio Arriba | Rio Arriba County, New Mexico | 751.17 | 16.06467 | 10.77458 | 0.038371 | 2.744807 | 0.038371 | 2.468536 | 2.355981 | 11.39619 | 2.312494 | NA | NA | 0.3069682 | 1.258569 | 6.781439 | 3.391998 | 4.279648 | 8.556738 | 7.50793 | 39006 | 293 | -106.6932 | 36.50962 |
Map the county level distribution of COVID-19 incidence rates, comparing to Figure 1 of the original study.
Unplanned deviation for reproduction: We also map the spatial distribution of the percent of people with any disability to improve our understanding of the geographic patterns and relationships of between the overarching independent variable (percentage of people with disability) and the dependent variable (COVID-19 incidence rate).
Calculate descriptive statistics for dependent COVID-19 rate and independent socio-demographic characteristics, reproducing the min, max, mean, and SD columns of original study table 1.
Planned deviation for reanalysis: We also calculate the Shapiro Wilk test for normality.
| min | max | mean | SD | ShapiroWilk | p | |
|---|---|---|---|---|---|---|
| covid_rate | 0.00 | 14257.17 | 966.90 | 1003.96 | 0.74 | 0 |
| dis_pct | 3.83 | 33.71 | 15.95 | 4.40 | 0.98 | 0 |
| white_pct | 0.85 | 33.26 | 13.55 | 4.63 | 0.98 | 0 |
| black_pct | 0.00 | 20.70 | 1.48 | 2.66 | 0.61 | 0 |
| native_pct | 0.00 | 13.74 | 0.28 | 0.94 | 0.28 | 0 |
| asian_pct | 0.00 | 3.45 | 0.09 | 0.18 | 0.51 | 0 |
| other_pct | 0.00 | 15.24 | 0.55 | 0.65 | 0.57 | 0 |
| non_hisp_white_pct | 0.10 | 33.16 | 12.84 | 4.81 | 0.99 | 0 |
| hisp_pct | 0.00 | 25.26 | 0.99 | 2.15 | 0.42 | 0 |
| non_hisp_non_white_pct | 0.00 | 20.93 | 2.13 | 2.75 | 0.70 | 0 |
| bpov_pct | 0.00 | 14.97 | 3.57 | 1.85 | 0.93 | 0 |
| apov_pct | 0.00 | 27.30 | 12.48 | 3.06 | 0.99 | 0 |
| pct_5_17 | 0.00 | 5.08 | 1.03 | 0.48 | 0.95 | 0 |
| pct_18_34 | 0.00 | 5.59 | 1.56 | 0.67 | 0.96 | 0 |
| pct_35_64 | 1.01 | 18.36 | 6.35 | 2.30 | 0.96 | 0 |
| pct_65_74 | 0.00 | 12.73 | 3.09 | 1.16 | 0.95 | 0 |
| pct_75 | 0.00 | 11.13 | 3.87 | 1.19 | 0.97 | 0 |
| male_pct | 1.30 | 18.19 | 8.06 | 2.37 | 0.98 | 0 |
| female_pct | 1.91 | 19.94 | 7.90 | 2.26 | 0.98 | 0 |
Compare reproduced descriptive statistics to original descriptive statistics. Difference is calculated as ‘reproduction study - original study’. Identical results will result in zero.
| min | max | mean | SD | |
|---|---|---|---|---|
| covid_rate | 0 | 0.17 | -0.1 | -0.04 |
| dis_pct | 0 | 0.00 | 0.0 | 0.00 |
| white_pct | 0 | 0.00 | 0.0 | 0.00 |
| black_pct | 0 | 0.00 | 0.0 | 0.00 |
| native_pct | 0 | 0.00 | 0.0 | 0.00 |
| asian_pct | 0 | 0.00 | 0.0 | 0.00 |
| other_pct | 0 | 0.00 | 0.0 | 0.00 |
| non_hisp_white_pct | 0 | 0.00 | 0.0 | 0.00 |
| hisp_pct | 0 | 0.00 | 0.0 | 0.00 |
| non_hisp_non_white_pct | 0 | 0.00 | 0.0 | 0.00 |
| bpov_pct | 0 | 0.00 | 0.0 | 0.00 |
| apov_pct | 0 | 0.00 | 0.0 | 0.00 |
| pct_5_17 | 0 | 0.00 | 0.0 | 0.00 |
| pct_18_34 | 0 | 0.00 | 0.0 | 0.00 |
| pct_35_64 | 0 | 0.00 | 0.0 | 0.00 |
| pct_65_74 | 0 | 0.00 | 0.0 | 0.00 |
| pct_75 | 0 | 0.00 | 0.0 | 0.00 |
| male_pct | 0 | 0.00 | 0.0 | 0.00 |
| female_pct | 0 | 0.00 | 0.0 | 0.00 |
The descriptive statistics are identical, except that the original study seems to have rounded the COVID-19 statistics to zero decimal places.
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Describe how the results are to be interpreted vis a vis each hypothesis or research question.
Include an integrity statement - The authors of this preregistration state that they completed this preregistration to the best of their knowledge and that no other preregistration exists pertaining to the same hypotheses and research. If a prior registration does exist, explain the rationale for revising the registration here.
Funding Name: name of funding for the projectFunding Title: title of project grantAward info URI: web address for award informationAward number: award numberThis report is based upon the template for Reproducible and Replicable Research in Human-Environment and Geographical Sciences, DOI:[10.17605/OSF.IO/W29MQ](https://doi.org/10.17605/OSF.IO/W29MQ)